These are quick notes about this topic that interests me. It’s an evolving article, so bare with me while I gather my thoughts and stitch them all together. From ChatGPT There is no single mathematical proof that neural networks work in general. However, there are many mathematical results that provide theoretical guarantees for the performance of neural networks under certain conditions. One of the key results in the theory of neural networks is the universal approximation theorem, which states that a feedforward neural network with a single hidden layer containing a sufficient number of units can approximate any continuous function to arbitrary accuracy.
Marvin Minsky, who was a pioneering computer scientist and artificial intelligence (AI) researcher, would likely have a complex and nuanced view on the topic of sentient AI. Minsky was a strong proponent of the idea that it is possible to create machines that are capable of thinking and reasoning like humans, and he believed that this was an important goal for AI research. In his book “The Society of Mind,” Minsky argued that the human mind is made up of many different interconnected processes, and that it is possible to create a machine that has a similar structure and function.
TL;DR GraphQL and gRPC are two different technologies that are used in software development to enable efficient communication between different systems. This article discusses the main differences between the two technologies, including their architecture, data formats, and language support. It also explains the pros and cons of using each technology, and compares and contrasts their use cases. Finally, the article provides examples of companies and organizations that have successfully used either GraphQL or gRPC in their systems.
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